Biostat 203B Homework 3

Due Feb 23 @ 11:59PM

Display machine information for reproducibility:

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

time zone: America/Los_Angeles
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.3.2    fastmap_1.1.1     cli_3.6.2        
 [5] tools_4.3.2       htmltools_0.5.7   rstudioapi_0.15.0 yaml_2.3.8       
 [9] rmarkdown_2.25    knitr_1.45        jsonlite_1.8.8    xfun_0.41        
[13] digest_0.6.34     rlang_1.1.3       evaluate_0.23    

Load necessary libraries (you can add more as needed).

library(arrow)

Attaching package: 'arrow'

The following object is masked from 'package:utils':

    timestamp

library(memuse)
library(pryr)
library(R.utils)

Loading required package: R.oo

Loading required package: R.methodsS3

R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.

R.oo v1.26.0 (2024-01-24 05:12:50 UTC) successfully loaded. See ?R.oo for help.

Attaching package: 'R.oo'

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Attaching package: 'R.utils'

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library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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library(ggrepel)
library(scales)

Attaching package: 'scales'

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Display your machine memory.

memuse::Sys.meminfo()

Totalram:  11.684 GiB 
Freeram:    9.007 GiB 

In this exercise, we use tidyverse (ggplot2, dplyr, etc) to explore the MIMIC-IV data introduced in homework 1 and to build a cohort of ICU stays.

Q1. Visualizing patient trajectory

Visualizing a patient’s encounters in a health care system is a common task in clinical data analysis. In this question, we will visualize a patient’s ADT (admission-discharge-transfer) history and ICU vitals in the MIMIC-IV data.

Q1.1 ADT history

A patient’s ADT history records the time of admission, discharge, and transfer in the hospital. This figure shows the ADT history of the patient with subject_id 10001217 in the MIMIC-IV data. The x-axis is the calendar time, and the y-axis is the type of event (ADT, lab, procedure). The color of the line segment represents the care unit. The size of the line segment represents whether the care unit is an ICU/CCU. The crosses represent lab events, and the shape of the dots represents the type of procedure. The title of the figure shows the patient’s demographic information and the subtitle shows top 3 diagnoses.

Do a similar visualization for the patient with subject_id 10013310 using ggplot.

Hint: We need to pull information from data files patients.csv.gz, admissions.csv.gz, transfers.csv.gz, labevents.csv.gz, procedures_icd.csv.gz, diagnoses_icd.csv.gz, d_icd_procedures.csv.gz, and d_icd_diagnoses.csv.gz. For the big file labevents.csv.gz, use the Parquet format you generated in Homework 2. For reproducibility, make the Parquet folder labevents_pq available at the current working directory hw3, for example, by a symbolic link. Make your code reproducible.

Answer: labevents.csv.gz in Parquet format:

#eval set to FALSE to avoid large file to reproduce in full set eval = TRUE
gzip -dk < ~/mimic/hosp/labevents.csv.gz > ./labevents.csv

#eval set to FALSE to avoid large file to reproduce in full set eval = TRUE
arrow::write_dataset(
  arrow::open_dataset("labevents.csv", format = "csv"),
  "./labevents.parquet", format = "parquet")

Patient of interest:

sid <- 10013310

Import csv files:

sid_adt <- read_csv("~/mimic/hosp/transfers.csv.gz") |>
  filter(subject_id == sid) |>
  print(width = Inf)

# A tibble: 14 × 7
   subject_id  hadm_id transfer_id eventtype
        <dbl>    <dbl>       <dbl> <chr>    
 1   10013310 21243435    31696219 discharge
 2   10013310 21243435    31736720 ED       
 3   10013310 21243435    33511674 transfer 
 4   10013310 21243435    34848129 transfer 
 5   10013310 21243435    38910974 admit    
 6   10013310 22098926    31651850 transfer 
 7   10013310 22098926    32769810 admit    
 8   10013310 22098926    33278851 transfer 
 9   10013310 22098926    34063502 ED       
10   10013310 22098926    36029206 discharge
11   10013310 27682188    30077870 transfer 
12   10013310 27682188    30444898 discharge
13   10013310 27682188    31203589 admit    
14   10013310 27682188    35160955 ED       
   careunit                                        intime             
   <chr>                                           <dttm>             
 1 <NA>                                            2153-06-05 19:58:00
 2 Emergency Department                            2153-05-26 08:56:00
 3 Medicine/Cardiology                             2153-05-26 16:19:26
 4 Medicine/Cardiology                             2153-05-26 14:42:55
 5 Medicine/Cardiology                             2153-05-26 14:18:39
 6 Neuro Intermediate                              2153-06-12 16:31:33
 7 Neuro Surgical Intensive Care Unit (Neuro SICU) 2153-06-10 11:55:42
 8 Medicine                                        2153-06-16 19:03:14
 9 Emergency Department                            2153-06-10 10:40:00
10 <NA>                                            2153-07-21 18:02:28
11 Medicine/Cardiology                             2153-05-07 20:47:19
12 <NA>                                            2153-05-13 15:36:52
13 Coronary Care Unit (CCU)                        2153-05-06 18:28:00
14 Emergency Department                            2153-05-06 10:21:00
   outtime            
   <dttm>             
 1 NA                 
 2 2153-05-26 14:18:39
 3 2153-06-05 19:58:00
 4 2153-05-26 16:19:26
 5 2153-05-26 14:42:55
 6 2153-06-16 19:03:14
 7 2153-06-12 16:31:33
 8 2153-07-21 18:02:28
 9 2153-06-10 11:55:42
10 NA                 
11 2153-05-13 15:36:52
12 NA                 
13 2153-05-07 20:47:19
14 2153-05-06 18:28:00

sid_adm <- read_csv("~/mimic/hosp/admissions.csv.gz") |>
  filter(subject_id == sid) |>
  print(width = Inf)

# A tibble: 3 × 16
  subject_id  hadm_id admittime           dischtime           deathtime
       <dbl>    <dbl> <dttm>              <dttm>              <dttm>   
1   10013310 21243435 2153-05-26 14:18:00 2153-06-05 19:30:00 NA       
2   10013310 22098926 2153-06-10 11:55:00 2153-07-21 18:00:00 NA       
3   10013310 27682188 2153-05-06 18:03:00 2153-05-13 13:45:00 NA       
  admission_type    admit_provider_id admission_location       
  <chr>             <chr>             <chr>                    
1 OBSERVATION ADMIT P78TNY            INFORMATION NOT AVAILABLE
2 OBSERVATION ADMIT P09IS0            INFORMATION NOT AVAILABLE
3 URGENT            P89ZCW            TRANSFER FROM HOSPITAL   
  discharge_location       insurance language marital_status race         
  <chr>                    <chr>     <chr>    <chr>          <chr>        
1 HOME HEALTH CARE         Medicare  ?        SINGLE         BLACK/AFRICAN
2 SKILLED NURSING FACILITY Medicare  ?        SINGLE         BLACK/AFRICAN
3 HOME HEALTH CARE         Medicare  ?        SINGLE         BLACK/AFRICAN
  edregtime           edouttime           hospital_expire_flag
  <dttm>              <dttm>                             <dbl>
1 2153-05-26 08:56:00 2153-05-26 16:33:00                    0
2 2153-06-10 10:40:00 2153-06-10 11:25:00                    0
3 2153-05-06 10:21:00 2153-05-06 18:28:00                    0

sid_lab <- 
  arrow::open_dataset("./labevents.parquet/part-0.parquet", 
    format = "parquet") |>
  filter(subject_id == sid) |>
  collect() |>
  print(width = Inf)

# A tibble: 2,285 × 16
   labevent_id subject_id hadm_id specimen_id itemid order_provider_id
         <int>      <int>   <int>       <int>  <int> <chr>            
 1      153564   10013310      NA     4841989  50887 ""               
 2      153565   10013310      NA     8958046  50934 ""               
 3      153566   10013310      NA     8958046  50947 ""               
 4      153567   10013310      NA     8958046  51003 ""               
 5      153568   10013310      NA     8958046  51678 ""               
 6      153569   10013310      NA    10682517  50933 ""               
 7      153570   10013310      NA    11713499  51133 ""               
 8      153571   10013310      NA    11713499  51146 ""               
 9      153572   10013310      NA    11713499  51200 ""               
10      153573   10013310      NA    11713499  51221 ""               
   charttime           storetime          
   <dttm>              <dttm>             
 1 2153-05-06 03:30:00 NA                 
 2 2153-05-06 03:30:00 2153-05-06 04:22:00
 3 2153-05-06 03:30:00 2153-05-06 04:22:00
 4 2153-05-06 03:30:00 2153-05-06 04:41:00
 5 2153-05-06 03:30:00 2153-05-06 04:22:00
 6 2153-05-06 03:30:00 NA                 
 7 2153-05-06 03:30:00 2153-05-06 04:09:00
 8 2153-05-06 03:30:00 2153-05-06 04:09:00
 9 2153-05-06 03:30:00 2153-05-06 04:09:00
10 2153-05-06 03:30:00 2153-05-06 04:09:00
   value                                    valuenum valueuom ref_range_lower
   <chr>                                       <dbl> <chr>              <dbl>
 1 HOLD.  DISCARD GREATER THAN 24 HRS OLD.     NA    ""                  NA  
 2 5                                            5    ""                  NA  
 3 2                                            2    ""                  NA  
 4 ___                                          2.97 "ng/mL"              0  
 5 14                                          14    ""                  NA  
 6 HOLD.  DISCARD GREATER THAN 4 HOURS OLD.    NA    ""                  NA  
 7 1.90                                         1.9  "K/uL"               1.2
 8 0.2                                          0.2  "%"                  0  
 9 0.1                                          0.1  "%"                  1  
10 32.5                                        32.5  "%"                 34  
   ref_range_upper flag       priority comments
             <dbl> <chr>      <chr>    <chr>   
 1           NA    ""         STAT     "___"   
 2           NA    ""         STAT     ""      
 3           NA    ""         STAT     ""      
 4            0.01 "abnormal" STAT     "___"   
 5           NA    ""         STAT     ""      
 6           NA    ""         STAT     "___"   
 7            3.7  ""         STAT     ""      
 8            1    ""         STAT     ""      
 9            7    "abnormal" STAT     ""      
10           45    "abnormal" STAT     ""      
# ℹ 2,275 more rows

d_icd_proc <- read_csv("~/mimic/hosp/d_icd_procedures.csv.gz") |>
  print(width = Inf)

# A tibble: 85,257 × 3
   icd_code icd_version
   <chr>          <dbl>
 1 0001               9
 2 0002               9
 3 0003               9
 4 0009               9
 5 001               10
 6 0010               9
 7 0011               9
 8 0012               9
 9 0013               9
10 0014               9
   long_title                                                 
   <chr>                                                      
 1 Therapeutic ultrasound of vessels of head and neck         
 2 Therapeutic ultrasound of heart                            
 3 Therapeutic ultrasound of peripheral vascular vessels      
 4 Other therapeutic ultrasound                               
 5 Central Nervous System and Cranial Nerves, Bypass          
 6 Implantation of chemotherapeutic agent                     
 7 Infusion of drotrecogin alfa (activated)                   
 8 Administration of inhaled nitric oxide                     
 9 Injection or infusion of nesiritide                        
10 Injection or infusion of oxazolidinone class of antibiotics
# ℹ 85,247 more rows

sid_proc <- read_csv("~/mimic/hosp/procedures_icd.csv.gz",
    col_types = cols(
      subject_id = col_integer(),
      hadm_id = col_integer(),
      seq_num = col_integer(),
      chartdate = col_datetime(),
      icd_code = col_character(),
      icd_version = col_integer())) |>
  filter(subject_id == sid) |>
  mutate(long_title = d_icd_proc$long_title[
    match(icd_code, d_icd_proc$icd_code)]) |>
  print(width = Inf)

# A tibble: 9 × 7
  subject_id  hadm_id seq_num chartdate           icd_code icd_version
       <int>    <int>   <int> <dttm>              <chr>          <int>
1   10013310 21243435       1 2153-05-27 00:00:00 4A023N7           10
2   10013310 21243435       2 2153-05-27 00:00:00 B2111ZZ           10
3   10013310 21243435       3 2153-05-27 00:00:00 B241ZZ3           10
4   10013310 22098926       1 2153-06-10 00:00:00 03CG3ZZ           10
5   10013310 22098926       2 2153-06-10 00:00:00 3E05317           10
6   10013310 22098926       3 2153-07-15 00:00:00 0DH63UZ           10
7   10013310 22098926       4 2153-06-11 00:00:00 3E0G76Z           10
8   10013310 27682188       1 2153-05-06 00:00:00 027034Z           10
9   10013310 27682188       2 2153-05-06 00:00:00 B211YZZ           10
  long_title                                                                    
  <chr>                                                                         
1 Measurement of Cardiac Sampling and Pressure, Left Heart, Percutaneous Approa…
2 Fluoroscopy of Multiple Coronary Arteries using Low Osmolar Contrast          
3 Ultrasonography of Multiple Coronary Arteries, Intravascular                  
4 Extirpation of Matter from Intracranial Artery, Percutaneous Approach         
5 Introduction of Other Thrombolytic into Peripheral Artery, Percutaneous Appro…
6 Insertion of Feeding Device into Stomach, Percutaneous Approach               
7 Introduction of Nutritional Substance into Upper GI, Via Natural or Artificia…
8 Dilation of Coronary Artery, One Artery with Drug-eluting Intraluminal Device…
9 Fluoroscopy of Multiple Coronary Arteries using Other Contrast                

sid_patients <- read_csv("~/mimic/hosp/patients.csv.gz") |>
  filter(subject_id == sid) |>
  print(width = Inf)

# A tibble: 1 × 6
  subject_id gender anchor_age anchor_year anchor_year_group dod       
       <dbl> <chr>       <dbl>       <dbl> <chr>             <date>    
1   10013310 F              70        2153 2017 - 2019       2153-11-19

d_icd_diag <- read_csv("~/mimic/hosp/d_icd_diagnoses.csv.gz") |>
  print(width = Inf)

# A tibble: 109,775 × 3
   icd_code icd_version long_title                           
   <chr>          <dbl> <chr>                                
 1 0010               9 Cholera due to vibrio cholerae       
 2 0011               9 Cholera due to vibrio cholerae el tor
 3 0019               9 Cholera, unspecified                 
 4 0020               9 Typhoid fever                        
 5 0021               9 Paratyphoid fever A                  
 6 0022               9 Paratyphoid fever B                  
 7 0023               9 Paratyphoid fever C                  
 8 0029               9 Paratyphoid fever, unspecified       
 9 0030               9 Salmonella gastroenteritis           
10 0031               9 Salmonella septicemia                
# ℹ 109,765 more rows

sid_diag <- read_csv("~/mimic/hosp/diagnoses_icd.csv.gz",
    col_types = cols(
      subject_id = col_integer(),
      hadm_id = col_integer(),
      seq_num = col_integer(),
      icd_code = col_character(),
      icd_version = col_integer())) |>
  filter(subject_id == sid) |>
  mutate(long_title = d_icd_diag$long_title[
    match(icd_code, d_icd_diag$icd_code)]) |>
  arrange(seq_num) |>
  print(width = Inf)

# A tibble: 71 × 6
   subject_id  hadm_id seq_num icd_code icd_version
        <int>    <int>   <int> <chr>          <int>
 1   10013310 21243435       1 I222              10
 2   10013310 22098926       1 I63412            10
 3   10013310 27682188       1 I2111             10
 4   10013310 21243435       2 I5023             10
 5   10013310 22098926       2 I618              10
 6   10013310 27682188       2 N170              10
 7   10013310 21243435       3 I428              10
 8   10013310 22098926       3 J690              10
 9   10013310 27682188       3 I5023             10
10   10013310 21243435       4 E1142             10
   long_title                                                                
   <chr>                                                                     
 1 Subsequent non-ST elevation (NSTEMI) myocardial infarction                
 2 Cerebral infarction due to embolism of left middle cerebral artery        
 3 ST elevation (STEMI) myocardial infarction involving right coronary artery
 4 Acute on chronic systolic (congestive) heart failure                      
 5 Other nontraumatic intracerebral hemorrhage                               
 6 Acute kidney failure with tubular necrosis                                
 7 Other cardiomyopathies                                                    
 8 Pneumonitis due to inhalation of food and vomit                           
 9 Acute on chronic systolic (congestive) heart failure                      
10 Type 2 diabetes mellitus with diabetic polyneuropathy                     
# ℹ 61 more rows

sid_adt |>
  filter(eventtype != "discharge") |>
  ggplot() +
  geom_segment(aes(
    x = intime, xend = outtime, 
    y = "ADT", yend = "ADT", 
    color = careunit,
    linewidth = str_detect(careunit, "(ICU|CCU)"))) +
  geom_point(data = sid_lab, 
    aes(
      x = charttime, 
      y = "Lab"),
    shape = 3) +
  geom_point(data = sid_proc, aes(
    x = chartdate, 
    y = "Procedure",
    shape = long_title)) +
  guides(linewidth = "none", shape = guide_legend(nrow = 9)) +
  theme_minimal() +
  theme(legend.position = "bottom",
    legend.box = "vertical") +
  labs(
    title = str_c("Patient ", sid, ", ",
      sid_patients$gender[1], ", ", 
      sid_patients$anchor_age[1], " years old, ",
      sid_adm$race[1]), 
    subtitle = str_c(sid_diag$long_title[1], "\n", 
      sid_diag$long_title[2], "\n", 
      sid_diag$long_title[3]),
    x = "",
    y = "",
    shape = "Procedure"
    ) +
  scale_y_discrete(limits = c("Procedure", "Lab", "ADT")) +
  scale_shape_manual(values = c(1:9))

Warning: Using linewidth for a discrete variable is not advised.

Q1.2 ICU stays

ICU stays are a subset of ADT history. This figure shows the vitals of the patient 10001217 during ICU stays. The x-axis is the calendar time, and the y-axis is the value of the vital. The color of the line represents the type of vital. The facet grid shows the abbreviation of the vital and the stay ID.

Do a similar visualization for the patient 10013310.

Answer:

#eval set to FALSE to avoid large file to reproduce in full set eval = TRUE
gzip -dk < ~/mimic/hosp/chartevents.csv.gz > ./chartevents.csv

#eval set to FALSE to avoid large file to reproduce in full set eval = TRUE
arrow::write_dataset(
  arrow::open_dataset("chartevents.csv", format = "csv"),
  "./chartevents.parquet", format = "parquet")

d_items <- read_csv("~/mimic/icu/d_items.csv.gz") |>
  print(width = Inf)

Rows: 4014 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): label, abbreviation, linksto, category, unitname, param_type
dbl (3): itemid, lownormalvalue, highnormalvalue

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# A tibble: 4,014 × 9
   itemid label                               abbreviation       linksto       
    <dbl> <chr>                               <chr>              <chr>         
 1 220001 Problem List                        Problem List       chartevents   
 2 220003 ICU Admission date                  ICU Admission date datetimeevents
 3 220045 Heart Rate                          HR                 chartevents   
 4 220046 Heart rate Alarm - High             HR Alarm - High    chartevents   
 5 220047 Heart Rate Alarm - Low              HR Alarm - Low     chartevents   
 6 220048 Heart Rhythm                        Heart Rhythm       chartevents   
 7 220050 Arterial Blood Pressure systolic    ABPs               chartevents   
 8 220051 Arterial Blood Pressure diastolic   ABPd               chartevents   
 9 220052 Arterial Blood Pressure mean        ABPm               chartevents   
10 220056 Arterial Blood Pressure Alarm - Low ABP Alarm - Low    chartevents   
   category            unitname param_type    lownormalvalue highnormalvalue
   <chr>               <chr>    <chr>                  <dbl>           <dbl>
 1 General             <NA>     Text                      NA              NA
 2 ADT                 <NA>     Date and time             NA              NA
 3 Routine Vital Signs bpm      Numeric                   NA              NA
 4 Alarms              bpm      Numeric                   NA              NA
 5 Alarms              bpm      Numeric                   NA              NA
 6 Routine Vital Signs <NA>     Text                      NA              NA
 7 Routine Vital Signs mmHg     Numeric                   90             140
 8 Routine Vital Signs mmHg     Numeric                   60              90
 9 Routine Vital Signs mmHg     Numeric                   NA              NA
10 Alarms              mmHg     Numeric                   NA              NA
# ℹ 4,004 more rows

sid_chart <- 
  arrow::open_dataset("./chartevents.parquet/part-0.parquet", 
    format = "parquet") |>
  filter(subject_id == sid) |>
  collect() |>
  mutate(abbreviation = d_items$abbreviation[
    match(itemid, d_items$itemid)]) |>
  filter(abbreviation %in% 
    c("HR", "NBPd", "NBPs", "RR", "Temperature F")) |>
  mutate(value = as.numeric(value)) |>
  arrange(itemid, charttime) |>
  print(width = Inf)

# A tibble: 549 × 12
   subject_id  hadm_id  stay_id caregiver_id charttime          
        <int>    <int>    <int>        <int> <dttm>             
 1   10013310 27682188 31203589        86936 2153-05-06 11:22:00
 2   10013310 27682188 31203589        86936 2153-05-06 11:25:00
 3   10013310 27682188 31203589         3749 2153-05-06 12:00:00
 4   10013310 27682188 31203589         3749 2153-05-06 13:00:00
 5   10013310 27682188 31203589         3749 2153-05-06 14:00:00
 6   10013310 27682188 31203589         3749 2153-05-06 15:00:00
 7   10013310 27682188 31203589         3749 2153-05-06 16:00:00
 8   10013310 27682188 31203589         3749 2153-05-06 17:00:00
 9   10013310 27682188 31203589         3749 2153-05-06 18:00:00
10   10013310 27682188 31203589         3749 2153-05-06 19:00:00
   storetime           itemid value valuenum valueuom warning abbreviation
   <dttm>               <int> <dbl>    <dbl> <chr>      <int> <chr>       
 1 2153-05-06 11:26:00 220045    56       56 bpm            0 HR          
 2 2153-05-06 11:26:00 220045    55       55 bpm            0 HR          
 3 2153-05-06 13:13:00 220045    58       58 bpm            0 HR          
 4 2153-05-06 13:13:00 220045    57       57 bpm            0 HR          
 5 2153-05-06 14:16:00 220045    57       57 bpm            0 HR          
 6 2153-05-06 15:18:00 220045    54       54 bpm            0 HR          
 7 2153-05-06 16:13:00 220045    55       55 bpm            0 HR          
 8 2153-05-06 18:15:00 220045    55       55 bpm            0 HR          
 9 2153-05-06 18:15:00 220045    56       56 bpm            0 HR          
10 2153-05-06 19:59:00 220045    55       55 bpm            0 HR          
# ℹ 539 more rows

sid_chart |>
  ggplot() +
  geom_point(aes(
    x = charttime, 
    y = value, 
    color = abbreviation)) +
  geom_line(aes(
    x = charttime, 
    y = value, 
    color = abbreviation)) +
  guides(color = "none") +
  facet_grid(abbreviation ~ stay_id, scales = "free") +
  scale_x_datetime(guide = guide_axis(n.dodge = 2)) +
  labs(
    title = str_c("Patient ", sid, " ICU Vitals"),
    x = "",
    y = "")+
  theme_bw() +
  theme(strip.text = element_text(
    size = 6))

Q2. ICU stays

icustays.csv.gz (https://mimic.mit.edu/docs/iv/modules/icu/icustays/) contains data about Intensive Care Units (ICU) stays. The first 10 lines are

zcat < ~/mimic/icu/icustays.csv.gz | head

subject_id,hadm_id,stay_id,first_careunit,last_careunit,intime,outtime,los
10000032,29079034,39553978,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2180-07-23 14:00:00,2180-07-23 23:50:47,0.4102662037037037
10000980,26913865,39765666,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2189-06-27 08:42:00,2189-06-27 20:38:27,0.4975347222222222
10001217,24597018,37067082,Surgical Intensive Care Unit (SICU),Surgical Intensive Care Unit (SICU),2157-11-20 19:18:02,2157-11-21 22:08:00,1.1180324074074075
10001217,27703517,34592300,Surgical Intensive Care Unit (SICU),Surgical Intensive Care Unit (SICU),2157-12-19 15:42:24,2157-12-20 14:27:41,0.9481134259259258
10001725,25563031,31205490,Medical/Surgical Intensive Care Unit (MICU/SICU),Medical/Surgical Intensive Care Unit (MICU/SICU),2110-04-11 15:52:22,2110-04-12 23:59:56,1.338587962962963
10001884,26184834,37510196,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2131-01-11 04:20:05,2131-01-20 08:27:30,9.171817129629629
10002013,23581541,39060235,Cardiac Vascular Intensive Care Unit (CVICU),Cardiac Vascular Intensive Care Unit (CVICU),2160-05-18 10:00:53,2160-05-19 17:33:33,1.3143518518518518
10002155,20345487,32358465,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2131-03-09 21:33:00,2131-03-10 18:09:21,0.8585763888888889
10002155,23822395,33685454,Coronary Care Unit (CCU),Coronary Care Unit (CCU),2129-08-04 12:45:00,2129-08-10 17:02:38,6.178912037037037

Q2.1 Ingestion

Import icustays.csv.gz as a tibble icustays_tble.

Answer:

icustays_tble <- read_csv("~/mimic/icu/icustays.csv.gz",
  col_types = cols(
    subject_id = col_integer()
  )) |>
  print(width = Inf)

# A tibble: 73,181 × 8
   subject_id  hadm_id  stay_id first_careunit                                  
        <int>    <dbl>    <dbl> <chr>                                           
 1   10000032 29079034 39553978 Medical Intensive Care Unit (MICU)              
 2   10000980 26913865 39765666 Medical Intensive Care Unit (MICU)              
 3   10001217 24597018 37067082 Surgical Intensive Care Unit (SICU)             
 4   10001217 27703517 34592300 Surgical Intensive Care Unit (SICU)             
 5   10001725 25563031 31205490 Medical/Surgical Intensive Care Unit (MICU/SICU)
 6   10001884 26184834 37510196 Medical Intensive Care Unit (MICU)              
 7   10002013 23581541 39060235 Cardiac Vascular Intensive Care Unit (CVICU)    
 8   10002155 20345487 32358465 Medical Intensive Care Unit (MICU)              
 9   10002155 23822395 33685454 Coronary Care Unit (CCU)                        
10   10002155 28994087 31090461 Medical/Surgical Intensive Care Unit (MICU/SICU)
   last_careunit                                    intime             
   <chr>                                            <dttm>             
 1 Medical Intensive Care Unit (MICU)               2180-07-23 14:00:00
 2 Medical Intensive Care Unit (MICU)               2189-06-27 08:42:00
 3 Surgical Intensive Care Unit (SICU)              2157-11-20 19:18:02
 4 Surgical Intensive Care Unit (SICU)              2157-12-19 15:42:24
 5 Medical/Surgical Intensive Care Unit (MICU/SICU) 2110-04-11 15:52:22
 6 Medical Intensive Care Unit (MICU)               2131-01-11 04:20:05
 7 Cardiac Vascular Intensive Care Unit (CVICU)     2160-05-18 10:00:53
 8 Medical Intensive Care Unit (MICU)               2131-03-09 21:33:00
 9 Coronary Care Unit (CCU)                         2129-08-04 12:45:00
10 Medical/Surgical Intensive Care Unit (MICU/SICU) 2130-09-24 00:50:00
   outtime               los
   <dttm>              <dbl>
 1 2180-07-23 23:50:47 0.410
 2 2189-06-27 20:38:27 0.498
 3 2157-11-21 22:08:00 1.12 
 4 2157-12-20 14:27:41 0.948
 5 2110-04-12 23:59:56 1.34 
 6 2131-01-20 08:27:30 9.17 
 7 2160-05-19 17:33:33 1.31 
 8 2131-03-10 18:09:21 0.859
 9 2129-08-10 17:02:38 6.18 
10 2130-09-27 22:13:41 3.89 
# ℹ 73,171 more rows

Q2.2 Summary and visualization

How many unique subject_id? Can a subject_id have multiple ICU stays? Summarize the number of ICU stays per subject_id by graphs.

Answer:

unique_subject_ids_count <- icustays_tble %>% 
  summarise(n_unique_subjects = n_distinct(subject_id)) %>% 
  pull(n_unique_subjects)
unique_subject_ids_count

[1] 50920

icustays_tble |>
  summarise(n_icu_stays = n(), .by = subject_id) |>
  ggplot() +
  geom_bar(aes(x = n_icu_stays, )) +
  labs(
    title = "Number of ICU stays per subject_id",
    x = "Number of ICU stays",
    y = "Count") +
  theme_minimal()

There are 50920 unique subject_id. A subject_id can have multiple ICU stays.

Q3. admissions data

Information of the patients admitted into hospital is available in admissions.csv.gz. See https://mimic.mit.edu/docs/iv/modules/hosp/admissions/ for details of each field in this file. The first 10 lines are

zcat < ~/mimic/hosp/admissions.csv.gz | head

subject_id,hadm_id,admittime,dischtime,deathtime,admission_type,admit_provider_id,admission_location,discharge_location,insurance,language,marital_status,race,edregtime,edouttime,hospital_expire_flag
10000032,22595853,2180-05-06 22:23:00,2180-05-07 17:15:00,,URGENT,P874LG,TRANSFER FROM HOSPITAL,HOME,Other,ENGLISH,WIDOWED,WHITE,2180-05-06 19:17:00,2180-05-06 23:30:00,0
10000032,22841357,2180-06-26 18:27:00,2180-06-27 18:49:00,,EW EMER.,P09Q6Y,EMERGENCY ROOM,HOME,Medicaid,ENGLISH,WIDOWED,WHITE,2180-06-26 15:54:00,2180-06-26 21:31:00,0
10000032,25742920,2180-08-05 23:44:00,2180-08-07 17:50:00,,EW EMER.,P60CC5,EMERGENCY ROOM,HOSPICE,Medicaid,ENGLISH,WIDOWED,WHITE,2180-08-05 20:58:00,2180-08-06 01:44:00,0
10000032,29079034,2180-07-23 12:35:00,2180-07-25 17:55:00,,EW EMER.,P30KEH,EMERGENCY ROOM,HOME,Medicaid,ENGLISH,WIDOWED,WHITE,2180-07-23 05:54:00,2180-07-23 14:00:00,0
10000068,25022803,2160-03-03 23:16:00,2160-03-04 06:26:00,,EU OBSERVATION,P51VDL,EMERGENCY ROOM,,Other,ENGLISH,SINGLE,WHITE,2160-03-03 21:55:00,2160-03-04 06:26:00,0
10000084,23052089,2160-11-21 01:56:00,2160-11-25 14:52:00,,EW EMER.,P6957U,WALK-IN/SELF REFERRAL,HOME HEALTH CARE,Medicare,ENGLISH,MARRIED,WHITE,2160-11-20 20:36:00,2160-11-21 03:20:00,0
10000084,29888819,2160-12-28 05:11:00,2160-12-28 16:07:00,,EU OBSERVATION,P63AD6,PHYSICIAN REFERRAL,,Medicare,ENGLISH,MARRIED,WHITE,2160-12-27 18:32:00,2160-12-28 16:07:00,0
10000108,27250926,2163-09-27 23:17:00,2163-09-28 09:04:00,,EU OBSERVATION,P38XXV,EMERGENCY ROOM,,Other,ENGLISH,SINGLE,WHITE,2163-09-27 16:18:00,2163-09-28 09:04:00,0
10000117,22927623,2181-11-15 02:05:00,2181-11-15 14:52:00,,EU OBSERVATION,P2358X,EMERGENCY ROOM,,Other,ENGLISH,DIVORCED,WHITE,2181-11-14 21:51:00,2181-11-15 09:57:00,0

Q3.1 Ingestion

Import admissions.csv.gz as a tibble admissions_tble.

Answer:

admissions_tble <- read_csv("~/mimic/hosp/admissions.csv.gz",
  col_types = cols(
    subject_id = col_integer()
  )) |>
  print(width = Inf)

# A tibble: 431,231 × 16
   subject_id  hadm_id admittime           dischtime           deathtime
        <int>    <dbl> <dttm>              <dttm>              <dttm>   
 1   10000032 22595853 2180-05-06 22:23:00 2180-05-07 17:15:00 NA       
 2   10000032 22841357 2180-06-26 18:27:00 2180-06-27 18:49:00 NA       
 3   10000032 25742920 2180-08-05 23:44:00 2180-08-07 17:50:00 NA       
 4   10000032 29079034 2180-07-23 12:35:00 2180-07-25 17:55:00 NA       
 5   10000068 25022803 2160-03-03 23:16:00 2160-03-04 06:26:00 NA       
 6   10000084 23052089 2160-11-21 01:56:00 2160-11-25 14:52:00 NA       
 7   10000084 29888819 2160-12-28 05:11:00 2160-12-28 16:07:00 NA       
 8   10000108 27250926 2163-09-27 23:17:00 2163-09-28 09:04:00 NA       
 9   10000117 22927623 2181-11-15 02:05:00 2181-11-15 14:52:00 NA       
10   10000117 27988844 2183-09-18 18:10:00 2183-09-21 16:30:00 NA       
   admission_type    admit_provider_id admission_location     discharge_location
   <chr>             <chr>             <chr>                  <chr>             
 1 URGENT            P874LG            TRANSFER FROM HOSPITAL HOME              
 2 EW EMER.          P09Q6Y            EMERGENCY ROOM         HOME              
 3 EW EMER.          P60CC5            EMERGENCY ROOM         HOSPICE           
 4 EW EMER.          P30KEH            EMERGENCY ROOM         HOME              
 5 EU OBSERVATION    P51VDL            EMERGENCY ROOM         <NA>              
 6 EW EMER.          P6957U            WALK-IN/SELF REFERRAL  HOME HEALTH CARE  
 7 EU OBSERVATION    P63AD6            PHYSICIAN REFERRAL     <NA>              
 8 EU OBSERVATION    P38XXV            EMERGENCY ROOM         <NA>              
 9 EU OBSERVATION    P2358X            EMERGENCY ROOM         <NA>              
10 OBSERVATION ADMIT P75S70            WALK-IN/SELF REFERRAL  HOME HEALTH CARE  
   insurance language marital_status race  edregtime          
   <chr>     <chr>    <chr>          <chr> <dttm>             
 1 Other     ENGLISH  WIDOWED        WHITE 2180-05-06 19:17:00
 2 Medicaid  ENGLISH  WIDOWED        WHITE 2180-06-26 15:54:00
 3 Medicaid  ENGLISH  WIDOWED        WHITE 2180-08-05 20:58:00
 4 Medicaid  ENGLISH  WIDOWED        WHITE 2180-07-23 05:54:00
 5 Other     ENGLISH  SINGLE         WHITE 2160-03-03 21:55:00
 6 Medicare  ENGLISH  MARRIED        WHITE 2160-11-20 20:36:00
 7 Medicare  ENGLISH  MARRIED        WHITE 2160-12-27 18:32:00
 8 Other     ENGLISH  SINGLE         WHITE 2163-09-27 16:18:00
 9 Other     ENGLISH  DIVORCED       WHITE 2181-11-14 21:51:00
10 Other     ENGLISH  DIVORCED       WHITE 2183-09-18 08:41:00
   edouttime           hospital_expire_flag
   <dttm>                             <dbl>
 1 2180-05-06 23:30:00                    0
 2 2180-06-26 21:31:00                    0
 3 2180-08-06 01:44:00                    0
 4 2180-07-23 14:00:00                    0
 5 2160-03-04 06:26:00                    0
 6 2160-11-21 03:20:00                    0
 7 2160-12-28 16:07:00                    0
 8 2163-09-28 09:04:00                    0
 9 2181-11-15 09:57:00                    0
10 2183-09-18 20:20:00                    0
# ℹ 431,221 more rows

Q3.2 Summary and visualization

Summarize the following information by graphics and explain any patterns you see.

• number of admissions per patient
  
• admission hour (anything unusual?)
  
• admission minute (anything unusual?)
  
• length of hospital stay (from admission to discharge) (anything unusual?)

According to the MIMIC-IV documentation,

All dates in the database have been shifted to protect patient confidentiality. Dates will be internally consistent for the same patient, but randomly distributed in the future. Dates of birth which occur in the present time are not true dates of birth. Furthermore, dates of birth which occur before the year 1900 occur if the patient is older than 89. In these cases, the patient’s age at their first admission has been fixed to 300.

Number of admissions per patient:

admissions_tble |>
  summarise(n_admissions = n(), .by = subject_id) |>
  ggplot() +
  geom_bar(aes(x = n_admissions)) +
  labs(
    title = "Number of admissions per patient",
    x = "Number of admissions",
    y = "Count") +
  scale_x_continuous(labels = comma) +
  theme_minimal()

Most patients only have one admission. There are a few patients with more than one admission.

Admission hour:

admissions_tble |>
  ggplot() +
  geom_bar(aes(x = hour(admittime))) +
  labs(
    title = "Admission hour",
    x = "Hour",
    y = "Count") +
  theme_minimal()

Most admissions occur in the afternoon to evening.

Admission minute:

admissions_tble |>
  ggplot() +
  geom_bar(aes(x = minute(admittime))) +
  labs(
    title = "Admission minute",
    x = "Minute",
    y = "Count") +
  theme_minimal()

There are more admissions at 0, 15, 30, and 45 minutes likely due to rounding when entering the data.

Length of hospital stay:

admissions_tble |>
  mutate(los = as.numeric(
    difftime(dischtime, admittime, units = "hours"))) |>
  ggplot() +
  geom_histogram(aes(x = los)) +
  labs(
    title = "Length of hospital stay",
    x = "Length of stay (hours)",
    y = "Count") +
  scale_y_continuous(labels = comma) +
  theme_minimal()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Most hospital stays are short and less than 100 hours. There are a few stays that are very long.

Q4. patients data

Patient information is available in patients.csv.gz. See https://mimic.mit.edu/docs/iv/modules/hosp/patients/ for details of each field in this file. The first 10 lines are

zcat < ~/mimic/hosp/patients.csv.gz | head

subject_id,gender,anchor_age,anchor_year,anchor_year_group,dod
10000032,F,52,2180,2014 - 2016,2180-09-09
10000048,F,23,2126,2008 - 2010,
10000068,F,19,2160,2008 - 2010,
10000084,M,72,2160,2017 - 2019,2161-02-13
10000102,F,27,2136,2008 - 2010,
10000108,M,25,2163,2014 - 2016,
10000115,M,24,2154,2017 - 2019,
10000117,F,48,2174,2008 - 2010,
10000178,F,59,2157,2017 - 2019,

Q4.1 Ingestion

Import patients.csv.gz (https://mimic.mit.edu/docs/iv/modules/hosp/patients/) as a tibble patients_tble.

patients_tble <- read_csv("~/mimic/hosp/patients.csv.gz") |>
  print(width = Inf)

Rows: 299712 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): gender, anchor_year_group
dbl  (3): subject_id, anchor_age, anchor_year
date (1): dod

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# A tibble: 299,712 × 6
   subject_id gender anchor_age anchor_year anchor_year_group dod       
        <dbl> <chr>       <dbl>       <dbl> <chr>             <date>    
 1   10000032 F              52        2180 2014 - 2016       2180-09-09
 2   10000048 F              23        2126 2008 - 2010       NA        
 3   10000068 F              19        2160 2008 - 2010       NA        
 4   10000084 M              72        2160 2017 - 2019       2161-02-13
 5   10000102 F              27        2136 2008 - 2010       NA        
 6   10000108 M              25        2163 2014 - 2016       NA        
 7   10000115 M              24        2154 2017 - 2019       NA        
 8   10000117 F              48        2174 2008 - 2010       NA        
 9   10000178 F              59        2157 2017 - 2019       NA        
10   10000248 M              34        2192 2014 - 2016       NA        
# ℹ 299,702 more rows

Q4.2 Summary and visualization

Summarize variables gender and anchor_age by graphics, and explain any patterns you see.

patients_tble |>
  ggplot() +
  geom_bar(aes(x = gender, color = gender, fill = gender)) +
  labs(
    title = "Gender of patients",
    y = "count") +
  theme_minimal()

There are more female patients than male.

patients_tble |>
  ggplot() +
  geom_histogram(aes(x = anchor_age), bins = 80) +
  labs(
    title = "Age of patients",
    x = "Age",
    y = "Count") +
  theme_minimal()

There is a large number of patients around 20 years old which decreases as age increases until around 50 years old where the number of patients starts to increase then decreases again. Unusually, there are a few age values without any patients.

Q5. Lab results

labevents.csv.gz (https://mimic.mit.edu/docs/iv/modules/hosp/labevents/) contains all laboratory measurements for patients. The first 10 lines are

zcat < ~/mimic/hosp/labevents.csv.gz | head

labevent_id,subject_id,hadm_id,specimen_id,itemid,order_provider_id,charttime,storetime,value,valuenum,valueuom,ref_range_lower,ref_range_upper,flag,priority,comments
1,10000032,,45421181,51237,P28Z0X,2180-03-23 11:51:00,2180-03-23 15:15:00,1.4,1.4,,0.9,1.1,abnormal,ROUTINE,
2,10000032,,45421181,51274,P28Z0X,2180-03-23 11:51:00,2180-03-23 15:15:00,___,15.1,sec,9.4,12.5,abnormal,ROUTINE,VERIFIED.
3,10000032,,52958335,50853,P28Z0X,2180-03-23 11:51:00,2180-03-25 11:06:00,___,15,ng/mL,30,60,abnormal,ROUTINE,NEW ASSAY IN USE ___: DETECTS D2 AND D3 25-OH ACCURATELY.
4,10000032,,52958335,50861,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,102,102,IU/L,0,40,abnormal,ROUTINE,
5,10000032,,52958335,50862,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,3.3,3.3,g/dL,3.5,5.2,abnormal,ROUTINE,
6,10000032,,52958335,50863,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,109,109,IU/L,35,105,abnormal,ROUTINE,
7,10000032,,52958335,50864,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,___,8,ng/mL,0,8.7,,ROUTINE,MEASURED BY ___.
8,10000032,,52958335,50868,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,12,12,mEq/L,8,20,,ROUTINE,
9,10000032,,52958335,50878,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,143,143,IU/L,0,40,abnormal,ROUTINE,

d_labitems.csv.gz (https://mimic.mit.edu/docs/iv/modules/hosp/d_labitems/) is the dictionary of lab measurements.

zcat < ~/mimic/hosp/d_labitems.csv.gz | head

itemid,label,fluid,category
50801,Alveolar-arterial Gradient,Blood,Blood Gas
50802,Base Excess,Blood,Blood Gas
50803,"Calculated Bicarbonate, Whole Blood",Blood,Blood Gas
50804,Calculated Total CO2,Blood,Blood Gas
50805,Carboxyhemoglobin,Blood,Blood Gas
50806,"Chloride, Whole Blood",Blood,Blood Gas
50808,Free Calcium,Blood,Blood Gas
50809,Glucose,Blood,Blood Gas
50810,"Hematocrit, Calculated",Blood,Blood Gas

We are interested in the lab measurements of creatinine (50912), potassium (50971), sodium (50983), chloride (50902), bicarbonate (50882), hematocrit (51221), white blood cell count (51301), and glucose (50931). Retrieve a subset of labevents.csv.gz that only containing these items for the patients in icustays_tble. Further restrict to the last available measurement (by storetime) before the ICU stay. The final labevents_tble should have one row per ICU stay and columns for each lab measurement.

Hint: Use the Parquet format you generated in Homework 2. For reproducibility, make labevents_pq folder available at the current working directory hw3, for example, by a symbolic link.

d_labitems <- read_csv("~/mimic/hosp/d_labitems.csv.gz") |>
  print(width = Inf)

Rows: 1622 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): label, fluid, category
dbl (1): itemid

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# A tibble: 1,622 × 4
   itemid label                               fluid category 
    <dbl> <chr>                               <chr> <chr>    
 1  50801 Alveolar-arterial Gradient          Blood Blood Gas
 2  50802 Base Excess                         Blood Blood Gas
 3  50803 Calculated Bicarbonate, Whole Blood Blood Blood Gas
 4  50804 Calculated Total CO2                Blood Blood Gas
 5  50805 Carboxyhemoglobin                   Blood Blood Gas
 6  50806 Chloride, Whole Blood               Blood Blood Gas
 7  50808 Free Calcium                        Blood Blood Gas
 8  50809 Glucose                             Blood Blood Gas
 9  50810 Hematocrit, Calculated              Blood Blood Gas
10  50811 Hemoglobin                          Blood Blood Gas
# ℹ 1,612 more rows

labevents_tble <- arrow::open_dataset(
  "./labevents.parquet/part-0.parquet", format = "parquet") |>
  select(subject_id, itemid, valuenum, storetime) |>
  filter(subject_id %in% icustays_tble$subject_id) |>
  filter(itemid %in% c(50912, 50971, 50983, 50902, 
    50882, 51221, 51301, 50931)) |>
  collect() |>
  mutate(subject_id = as.integer(subject_id)) |>
  left_join(icustays_tble, by = c("subject_id")) |>
  select(subject_id, stay_id, itemid, valuenum, storetime, intime) |>
  mutate(itemname = d_labitems$label[
    match(itemid, d_labitems$itemid)]) |>
  filter(storetime <= intime) |>
  group_by(subject_id, stay_id, itemid) |>
  arrange(storetime, by_group = T) |>
  slice_tail(n = 1) |>
  ungroup() |>
  select(subject_id,stay_id, itemname, valuenum) |>
  pivot_wider(names_from = itemname, values_from = valuenum,
    values_fn = list(valuenum = ~ .[1])) |>
  print(width = Inf)

Warning in left_join(mutate(collect(filter(filter(select(arrow::open_dataset("./labevents.parquet/part-0.parquet", : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 845 of `x` matches multiple rows in `y`.
ℹ Row 1 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

# A tibble: 68,468 × 10
   subject_id  stay_id Bicarbonate Chloride Creatinine Glucose Potassium Sodium
        <int>    <dbl>       <dbl>    <dbl>      <dbl>   <dbl>     <dbl>  <dbl>
 1   10000032 39553978          25       95        0.7     102       6.7    126
 2   10000980 39765666          21      109        2.3      89       3.9    144
 3   10001217 34592300          30      104        0.5      87       4.1    142
 4   10001217 37067082          22      108        0.6     112       4.2    142
 5   10001725 31205490          NA       98       NA        NA       4.1    139
 6   10001884 37510196          30       88        1.1     141       4.5    130
 7   10002013 39060235          24      102        0.9     288       3.5    137
 8   10002155 31090461          23       98        2.8     117       4.9    135
 9   10002155 32358465          26       85        1.4     133       5.7    120
10   10002155 33685454          24      105        1.1     138       4.6    139
   Hematocrit `White Blood Cells`
        <dbl>               <dbl>
 1       41.1                 6.9
 2       27.3                 5.3
 3       37.4                 5.4
 4       38.1                15.7
 5       NA                  NA  
 6       39.7                12.2
 7       34.9                 7.2
 8       25.5                17.9
 9       22.4                 9.8
10       39.7                 7.9
# ℹ 68,458 more rows

Q6. Vitals from charted events

chartevents.csv.gz (https://mimic.mit.edu/docs/iv/modules/icu/chartevents/) contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The itemid variable indicates a single measurement type in the database. The value variable is the value measured for itemid. The first 10 lines of chartevents.csv.gz are

zcat < ~/mimic/icu/chartevents.csv.gz | head

subject_id,hadm_id,stay_id,caregiver_id,charttime,storetime,itemid,value,valuenum,valueuom,warning
10000032,29079034,39553978,47007,2180-07-23 21:01:00,2180-07-23 22:15:00,220179,82,82,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 21:01:00,2180-07-23 22:15:00,220180,59,59,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 21:01:00,2180-07-23 22:15:00,220181,63,63,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220045,94,94,bpm,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220179,85,85,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220180,55,55,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220181,62,62,mmHg,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220210,20,20,insp/min,0
10000032,29079034,39553978,47007,2180-07-23 22:00:00,2180-07-23 22:15:00,220277,95,95,%,0

d_items.csv.gz (https://mimic.mit.edu/docs/iv/modules/icu/d_items/) is the dictionary for the itemid in chartevents.csv.gz.

zcat < ~/mimic/icu/d_items.csv.gz | head

itemid,label,abbreviation,linksto,category,unitname,param_type,lownormalvalue,highnormalvalue
220001,Problem List,Problem List,chartevents,General,,Text,,
220003,ICU Admission date,ICU Admission date,datetimeevents,ADT,,Date and time,,
220045,Heart Rate,HR,chartevents,Routine Vital Signs,bpm,Numeric,,
220046,Heart rate Alarm - High,HR Alarm - High,chartevents,Alarms,bpm,Numeric,,
220047,Heart Rate Alarm - Low,HR Alarm - Low,chartevents,Alarms,bpm,Numeric,,
220048,Heart Rhythm,Heart Rhythm,chartevents,Routine Vital Signs,,Text,,
220050,Arterial Blood Pressure systolic,ABPs,chartevents,Routine Vital Signs,mmHg,Numeric,90,140
220051,Arterial Blood Pressure diastolic,ABPd,chartevents,Routine Vital Signs,mmHg,Numeric,60,90
220052,Arterial Blood Pressure mean,ABPm,chartevents,Routine Vital Signs,mmHg,Numeric,,

We are interested in the vitals for ICU patients: heart rate (220045), systolic non-invasive blood pressure (220179), diastolic non-invasive blood pressure (220180), body temperature in Fahrenheit (223761), and respiratory rate (220210). Retrieve a subset of chartevents.csv.gz only containing these items for the patients in icustays_tble. Further restrict to the first vital measurement within the ICU stay. The final chartevents_tble should have one row per ICU stay and columns for each vital measurement.

Hint: Use the Parquet format you generated in Homework 2. For reproducibility, make chartevents_pq folder available at the current working directory, for example, by a symbolic link.

chart_tble <- arrow::open_dataset(
    "./chartevents.parquet/part-0.parquet", format = "parquet") |>
  select(subject_id, stay_id, itemid, valuenum, charttime) |>
  filter(subject_id %in% icustays_tble$subject_id) |>
  filter(itemid %in% c(220045, 220179, 220180, 223761, 220210)) |>
  collect() |>
  mutate(subject_id = as.integer(subject_id)) |>
  left_join(icustays_tble, by = c("subject_id", "stay_id")) |>
  select(subject_id, stay_id, itemid, valuenum, charttime, intime) |>
  mutate(itemname = d_items$label[match(itemid, d_items$itemid)]) |>
  filter(charttime >= intime) |>
  group_by(subject_id, stay_id, itemid) |>
  arrange(charttime, by_group = T) |>
  slice_head(n = 1) |>
  ungroup() |>
  select(subject_id,stay_id, itemname, valuenum) |>
  pivot_wider(names_from = itemname, values_from = valuenum,
    values_fn = list(valuenum = ~ .[1])) |>
  print(width = Inf)

# A tibble: 73,164 × 7
   subject_id  stay_id `Heart Rate` `Non Invasive Blood Pressure systolic`
        <int>    <dbl>        <dbl>                                  <dbl>
 1   10000032 39553978           91                                     84
 2   10000980 39765666           77                                    150
 3   10001217 34592300           96                                    167
 4   10001217 37067082           86                                    151
 5   10001725 31205490           55                                     73
 6   10001884 37510196           38                                    180
 7   10002013 39060235           80                                    104
 8   10002155 31090461           94                                    118
 9   10002155 32358465           98                                    109
10   10002155 33685454           68                                    126
   `Non Invasive Blood Pressure diastolic` `Respiratory Rate`
                                     <dbl>              <dbl>
 1                                      48                 24
 2                                      77                 23
 3                                      95                 11
 4                                      90                 18
 5                                      56                 19
 6                                      12                 10
 7                                      70                 14
 8                                      51                 18
 9                                      65                 23
10                                      61                 18
   `Temperature Fahrenheit`
                      <dbl>
 1                     98.7
 2                     98  
 3                     97.6
 4                     98.5
 5                     97.7
 6                     98.1
 7                     97.2
 8                     96.9
 9                     97.7
10                     95.9
# ℹ 73,154 more rows

Q7. Putting things together

Let us create a tibble mimic_icu_cohort for all ICU stays, where rows are all ICU stays of adults (age at intime >= 18) and columns contain at least following variables

• all variables in icustays_tble
  
• all variables in admissions_tble
  
• all variables in patients_tble
• the last lab measurements before the ICU stay in labevents_tble
• the first vital measurements during the ICU stay in chartevents_tble

The final mimic_icu_cohort should have one row per ICU stay and columns for each variable.

mimic_icu_cohort <- icustays_tble |>
  left_join(admissions_tble, by = c("subject_id", "hadm_id")) |>
  left_join(patients_tble, by = c("subject_id")) |>
  mutate(age_intime = year(intime)-anchor_year + anchor_age) |>
  filter(age_intime >= 18) |>
  left_join(labevents_tble, by = c("subject_id", "stay_id")) |>
  left_join(chart_tble, by = c("subject_id", "stay_id")) |>
  print(width = Inf)

# A tibble: 73,181 × 41
   subject_id  hadm_id  stay_id first_careunit                                  
        <dbl>    <dbl>    <dbl> <chr>                                           
 1   10000032 29079034 39553978 Medical Intensive Care Unit (MICU)              
 2   10000980 26913865 39765666 Medical Intensive Care Unit (MICU)              
 3   10001217 24597018 37067082 Surgical Intensive Care Unit (SICU)             
 4   10001217 27703517 34592300 Surgical Intensive Care Unit (SICU)             
 5   10001725 25563031 31205490 Medical/Surgical Intensive Care Unit (MICU/SICU)
 6   10001884 26184834 37510196 Medical Intensive Care Unit (MICU)              
 7   10002013 23581541 39060235 Cardiac Vascular Intensive Care Unit (CVICU)    
 8   10002155 20345487 32358465 Medical Intensive Care Unit (MICU)              
 9   10002155 23822395 33685454 Coronary Care Unit (CCU)                        
10   10002155 28994087 31090461 Medical/Surgical Intensive Care Unit (MICU/SICU)
   last_careunit                                    intime             
   <chr>                                            <dttm>             
 1 Medical Intensive Care Unit (MICU)               2180-07-23 14:00:00
 2 Medical Intensive Care Unit (MICU)               2189-06-27 08:42:00
 3 Surgical Intensive Care Unit (SICU)              2157-11-20 19:18:02
 4 Surgical Intensive Care Unit (SICU)              2157-12-19 15:42:24
 5 Medical/Surgical Intensive Care Unit (MICU/SICU) 2110-04-11 15:52:22
 6 Medical Intensive Care Unit (MICU)               2131-01-11 04:20:05
 7 Cardiac Vascular Intensive Care Unit (CVICU)     2160-05-18 10:00:53
 8 Medical Intensive Care Unit (MICU)               2131-03-09 21:33:00
 9 Coronary Care Unit (CCU)                         2129-08-04 12:45:00
10 Medical/Surgical Intensive Care Unit (MICU/SICU) 2130-09-24 00:50:00
   outtime               los admittime           dischtime          
   <dttm>              <dbl> <dttm>              <dttm>             
 1 2180-07-23 23:50:47 0.410 2180-07-23 12:35:00 2180-07-25 17:55:00
 2 2189-06-27 20:38:27 0.498 2189-06-27 07:38:00 2189-07-03 03:00:00
 3 2157-11-21 22:08:00 1.12  2157-11-18 22:56:00 2157-11-25 18:00:00
 4 2157-12-20 14:27:41 0.948 2157-12-18 16:58:00 2157-12-24 14:55:00
 5 2110-04-12 23:59:56 1.34  2110-04-11 15:08:00 2110-04-14 15:00:00
 6 2131-01-20 08:27:30 9.17  2131-01-07 20:39:00 2131-01-20 05:15:00
 7 2160-05-19 17:33:33 1.31  2160-05-18 07:45:00 2160-05-23 13:30:00
 8 2131-03-10 18:09:21 0.859 2131-03-09 20:33:00 2131-03-10 01:55:00
 9 2129-08-10 17:02:38 6.18  2129-08-04 12:44:00 2129-08-18 16:53:00
10 2130-09-27 22:13:41 3.89  2130-09-23 21:59:00 2130-09-29 18:55:00
   deathtime           admission_type              admit_provider_id
   <dttm>              <chr>                       <chr>            
 1 NA                  EW EMER.                    P30KEH           
 2 NA                  EW EMER.                    P30KEH           
 3 NA                  EW EMER.                    P4645A           
 4 NA                  DIRECT EMER.                P99698           
 5 NA                  EW EMER.                    P35SU0           
 6 2131-01-20 05:15:00 OBSERVATION ADMIT           P874LG           
 7 NA                  SURGICAL SAME DAY ADMISSION P47E1G           
 8 2131-03-10 21:53:00 EW EMER.                    P80515           
 9 NA                  EW EMER.                    P05HUO           
10 NA                  EW EMER.                    P3529J           
   admission_location discharge_location           insurance language
   <chr>              <chr>                        <chr>     <chr>   
 1 EMERGENCY ROOM     HOME                         Medicaid  ENGLISH 
 2 EMERGENCY ROOM     HOME HEALTH CARE             Medicare  ENGLISH 
 3 EMERGENCY ROOM     HOME HEALTH CARE             Other     ?       
 4 PHYSICIAN REFERRAL HOME HEALTH CARE             Other     ?       
 5 PACU               HOME                         Other     ENGLISH 
 6 EMERGENCY ROOM     DIED                         Medicare  ENGLISH 
 7 PHYSICIAN REFERRAL HOME HEALTH CARE             Medicare  ENGLISH 
 8 EMERGENCY ROOM     DIED                         Other     ENGLISH 
 9 PROCEDURE SITE     CHRONIC/LONG TERM ACUTE CARE Other     ENGLISH 
10 EMERGENCY ROOM     HOME HEALTH CARE             Other     ENGLISH 
   marital_status race                   edregtime           edouttime          
   <chr>          <chr>                  <dttm>              <dttm>             
 1 WIDOWED        WHITE                  2180-07-23 05:54:00 2180-07-23 14:00:00
 2 MARRIED        BLACK/AFRICAN AMERICAN 2189-06-27 06:25:00 2189-06-27 08:42:00
 3 MARRIED        WHITE                  2157-11-18 17:38:00 2157-11-19 01:24:00
 4 MARRIED        WHITE                  NA                  NA                 
 5 MARRIED        WHITE                  NA                  NA                 
 6 MARRIED        BLACK/AFRICAN AMERICAN 2131-01-07 13:36:00 2131-01-07 22:13:00
 7 SINGLE         OTHER                  NA                  NA                 
 8 MARRIED        WHITE                  2131-03-09 19:14:00 2131-03-09 21:33:00
 9 MARRIED        WHITE                  2129-08-04 11:00:00 2129-08-04 12:35:00
10 MARRIED        WHITE                  2130-09-23 19:59:00 2130-09-24 00:50:00
   hospital_expire_flag gender anchor_age anchor_year anchor_year_group
                  <dbl> <chr>       <dbl>       <dbl> <chr>            
 1                    0 F              52        2180 2014 - 2016      
 2                    0 F              73        2186 2008 - 2010      
 3                    0 F              55        2157 2011 - 2013      
 4                    0 F              55        2157 2011 - 2013      
 5                    0 F              46        2110 2011 - 2013      
 6                    1 F              68        2122 2008 - 2010      
 7                    0 F              53        2156 2008 - 2010      
 8                    1 F              80        2128 2008 - 2010      
 9                    0 F              80        2128 2008 - 2010      
10                    0 F              80        2128 2008 - 2010      
   dod        age_intime Bicarbonate Chloride Creatinine Glucose Potassium
   <date>          <dbl>       <dbl>    <dbl>      <dbl>   <dbl>     <dbl>
 1 2180-09-09         52          25       95        0.7     102       6.7
 2 2193-08-26         76          21      109        2.3      89       3.9
 3 NA                 55          22      108        0.6     112       4.2
 4 NA                 55          30      104        0.5      87       4.1
 5 NA                 46          NA       98       NA        NA       4.1
 6 2131-01-20         77          30       88        1.1     141       4.5
 7 NA                 57          24      102        0.9     288       3.5
 8 2131-03-10         83          26       85        1.4     133       5.7
 9 2131-03-10         81          24      105        1.1     138       4.6
10 2131-03-10         82          23       98        2.8     117       4.9
   Sodium Hematocrit `White Blood Cells` `Heart Rate`
    <dbl>      <dbl>               <dbl>        <dbl>
 1    126       41.1                 6.9           91
 2    144       27.3                 5.3           77
 3    142       38.1                15.7           86
 4    142       37.4                 5.4           96
 5    139       NA                  NA             55
 6    130       39.7                12.2           38
 7    137       34.9                 7.2           80
 8    120       22.4                 9.8           98
 9    139       39.7                 7.9           68
10    135       25.5                17.9           94
   `Non Invasive Blood Pressure systolic`
                                    <dbl>
 1                                     84
 2                                    150
 3                                    151
 4                                    167
 5                                     73
 6                                    180
 7                                    104
 8                                    109
 9                                    126
10                                    118
   `Non Invasive Blood Pressure diastolic` `Respiratory Rate`
                                     <dbl>              <dbl>
 1                                      48                 24
 2                                      77                 23
 3                                      90                 18
 4                                      95                 11
 5                                      56                 19
 6                                      12                 10
 7                                      70                 14
 8                                      65                 23
 9                                      61                 18
10                                      51                 18
   `Temperature Fahrenheit`
                      <dbl>
 1                     98.7
 2                     98  
 3                     98.5
 4                     97.6
 5                     97.7
 6                     98.1
 7                     97.2
 8                     97.7
 9                     95.9
10                     96.9
# ℹ 73,171 more rows

Q8. Exploratory data analysis (EDA)

Summarize the following information about the ICU stay cohort mimic_icu_cohort using appropriate numerics or graphs:

• Length of ICU stay los vs demographic variables (race, insurance, marital_status, gender, age at intime)

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = age_intime, y = los)) +
  labs(
    title = "Length of ICU stay vs age at intime",
    x = "Age at intime",
    y = "Length of ICU stay")

mimic_icu_cohort |>
  ggplot() +
  geom_violin(aes(x = race, y = los)) +
  labs(
    title = "Length of ICU stay vs race",
    x = "",
    y = "Length of ICU stay") +
  theme(axis.text.x = element_text(
    size = 7, angle = 90, vjust = 0.5, hjust=1))

mimic_icu_cohort |>
  ggplot() +
  geom_violin(aes(x = insurance, y = los)) +
  labs(
    title = "Length of ICU stay vs insurance",
    x = "",
    y = "Length of ICU stay") +
  theme(axis.text.x = element_text(
    size = 7, angle = 90, vjust = 0.5, hjust=1))

mimic_icu_cohort |>
  ggplot() +
  geom_violin(aes(x = marital_status, y = los)) +
  labs(
    title = "Length of ICU stay vs marital status",
    x = "",
    y = "Length of ICU stay") +
  theme(axis.text.x = element_text(
    size = 7, angle = 90, vjust = 0.5, hjust=1))

mimic_icu_cohort |> 
  ggplot() +
  geom_violin(aes(x = gender, y = los)) +
  labs(
    title = "Length of ICU stay vs gender",
    x = "",
    y = "Length of ICU stay") +
  scale_x_discrete(labels = c("Female", "Male")) +
  theme(axis.text.x = element_text(
    size = 7, angle = 90, vjust = 0.5, hjust=1))

• Length of ICU stay los vs the last available lab measurements before ICU stay

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = Creatinine, y = los)) +
  labs(
    title = "Length of ICU stay vs creatinine",
    x = "Creatinine",
    y = "Length of ICU stay")

Warning: Removed 5754 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = Potassium, y = los)) +
  labs(
    title = "Length of ICU stay vs potassium",
    x = "Potassium",
    y = "Length of ICU stay")

Warning: Removed 8888 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = Sodium, y = los)) +
  labs(
    title = "Length of ICU stay vs sodium",
    x = "Sodium",
    y = "Length of ICU stay")

Warning: Removed 8858 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = Chloride, y = los)) +
  labs(
    title = "Length of ICU stay vs chloride",
    x = "Chloride",
    y = "Length of ICU stay")

Warning: Removed 8869 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = Bicarbonate, y = los)) +
  labs(
    title = "Length of ICU stay vs bicarbonate",
    x = "Bicarbonate",
    y = "Length of ICU stay")

Warning: Removed 9035 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = Hematocrit, y = los)) +
  labs(
    title = "Length of ICU stay vs hematocrit",
    x = "Hematocrit",
    y = "Length of ICU stay")

Warning: Removed 5015 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = `White Blood Cells`, y = los)) +
  labs(
    title = "Length of ICU stay vs white blood cell count",
    x = "White Blood Cell Count",
    y = "Length of ICU stay")

Warning: Removed 5092 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = Glucose, y = los)) +
  labs(
    title = "Length of ICU stay vs glucose",
    x = "Glucose",
    y = "Length of ICU stay")

Warning: Removed 9084 rows containing missing values (`geom_point()`).

• Length of ICU stay los vs the average vital measurements within the first hour of ICU stay

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = `Heart Rate`, y = los)) +
  labs(
    title = "Length of ICU stay vs heart rate",
    x = "Heart Rate",
    y = "Length of ICU stay")

Warning: Removed 18 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = `Non Invasive Blood Pressure systolic`, y = los)) +
  labs(
    title = "Length of ICU stay vs systolic blood pressure",
    x = "Systolic Blood Pressure",
    y = "Length of ICU stay")

Warning: Removed 970 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = `Non Invasive Blood Pressure diastolic`, y = los)) +
  labs(
    title = "Length of ICU stay vs diastolic blood pressure",
    x = "Diastolic Blood Pressure",
    y = "Length of ICU stay")

Warning: Removed 974 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  ggplot() +
  geom_point(aes(x = `Respiratory Rate`, y = los)) +
  labs(
    title = "Length of ICU stay vs respiratory rate",
    x = "Respiratory Rate",
    y = "Length of ICU stay")

Warning: Removed 95 rows containing missing values (`geom_point()`).

mimic_icu_cohort |>
  mutate(`Temperature Fahrenheit` = 
    as.numeric(`Temperature Fahrenheit`)) |>
  ggplot() +
  geom_point(aes(x = `Temperature Fahrenheit`, y = los)) +
  labs(
    title = "Length of ICU stay vs temperature",
    x = "Temperature",
    y = "Length of ICU stay")

Warning: Removed 1353 rows containing missing values (`geom_point()`).

• Length of ICU stay los vs first ICU unit

mimic_icu_cohort |>
  ggplot() +
  geom_violin(aes(x = first_careunit, y = los)) +
  labs(
    title = "Length of ICU stay vs first care unit",
    x = "First care unit",
    y = "Length of ICU stay") +
  theme(axis.text.x = element_text(
    size = 7, angle = 90, vjust = 0.5, hjust=1))